{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d751b199-44cc-41df-87bc-2304f0b43c44",
   "metadata": {},
   "source": [
    "# Setup imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8e6ffd47-e1d5-4d29-9a89-1f9b5dbd36a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-06-07 18:03:35.724824: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
      "2024-06-07 18:03:35.753970: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
      "To enable the following instructions: AVX2 AVX_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n",
      "2024-06-07 18:03:36.253032: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MONAI version: 1.4.dev2421\n",
      "Numpy version: 1.26.4\n",
      "Pytorch version: 2.3.0+cu121\n",
      "MONAI flags: HAS_EXT = False, USE_COMPILED = False, USE_META_DICT = False\n",
      "MONAI rev id: 1070036ea3c30176fc82cfb15952387bed8b8a90\n",
      "MONAI __file__: /home/<username>/.local/lib/python3.10/site-packages/monai/__init__.py\n",
      "\n",
      "Optional dependencies:\n",
      "Pytorch Ignite version: 0.4.11\n",
      "ITK version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "Nibabel version: 5.2.1\n",
      "scikit-image version: 0.23.2\n",
      "scipy version: 1.13.0\n",
      "Pillow version: 10.3.0\n",
      "Tensorboard version: 2.16.2\n",
      "gdown version: 5.2.0\n",
      "TorchVision version: 0.18.0+cu121\n",
      "tqdm version: 4.66.4\n",
      "lmdb version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "psutil version: 5.9.8\n",
      "pandas version: 2.2.2\n",
      "einops version: 0.8.0\n",
      "transformers version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "mlflow version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "pynrrd version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "clearml version: NOT INSTALLED or UNKNOWN VERSION.\n",
      "\n",
      "For details about installing the optional dependencies, please visit:\n",
      "    https://docs.monai.io/en/latest/installation.html#installing-the-recommended-dependencies\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from monai.utils import first, set_determinism\n",
    "from monai.data import *\n",
    "from monai.transforms import *\n",
    "from monai.transforms import (\n",
    "    Activationsd,\n",
    "    EnsureChannelFirstd,\n",
    "    EnsureTyped,\n",
    "    AsDiscreted,\n",
    "    Compose,\n",
    "    Invertd,\n",
    "    LoadImaged,\n",
    "    Orientationd,\n",
    "    Resized,\n",
    "    SaveImaged,\n",
    "    ScaleIntensityd,\n",
    ")\n",
    "from monai.handlers.utils import from_engine\n",
    "# https://docs.monai.io/en/stable/networks.html#nets\n",
    "from monai.networks.nets import UNet,AttentionUnet, DynUNet, SegResNet, VNet, SegResNetVAE, UNETR\n",
    "from monai.networks.layers import Norm\n",
    "from monai.metrics import DiceMetric\n",
    "from monai.losses import DiceLoss\n",
    "from monai.losses import DiceCELoss\n",
    "from monai.inferers import sliding_window_inference\n",
    "from monai.data import CacheDataset, DataLoader, Dataset, decollate_batch\n",
    "from monai.config import print_config\n",
    "from monai.apps import download_and_extract\n",
    "import aim\n",
    "from aim.pytorch import track_gradients_dists, track_params_dists\n",
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "import tempfile\n",
    "import shutil\n",
    "import os\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b6f1755f-3cf4-4c00-92da-21dbb65174b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "trdata_dir = '/mnt/datawow/kw/verse/IndVertSeg_VerSe-master/Data/Training'\n",
    "train_images = sorted(glob.glob(os.path.join(trdata_dir, \"images\", \"*.nii.gz\")))\n",
    "train_labels = sorted(glob.glob(os.path.join(trdata_dir, \"masks\", \"*.nii.gz\")))\n",
    "train_files = [{\"image\": image_name, \"label\": label_name} for image_name, label_name in zip(train_images, train_labels)][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4cfcf268-88ed-4f88-9faa-6b1435a4ef12",
   "metadata": {},
   "outputs": [],
   "source": [
    "validdata_dir = '/mnt/datawow/kw/verse/IndVertSeg_VerSe-master/Data/Validation'\n",
    "val_images = sorted(glob.glob(os.path.join(validdata_dir, \"images\", \"*.nii.gz\")))\n",
    "val_labels = sorted(glob.glob(os.path.join(validdata_dir, \"masks\", \"*.nii.gz\")))\n",
    "val_files = [{\"image\": image_name, \"label\": label_name} for image_name, label_name in zip(val_images, val_labels)][:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c567318e-4ed4-4751-9557-49f1d82863b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20, 20)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(train_files), len(val_files)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b67de5e4-29fc-46a3-8fd3-0209ecf1673e",
   "metadata": {},
   "source": [
    "# Set deterministic training for reproducibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "5c35e71a-03c4-4afc-a143-4ce2a90c1c5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "set_determinism(seed=1645)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ba32469c-f001-40f5-a294-abb1ba68b37c",
   "metadata": {},
   "source": [
    "## Setup transforms for training and validation\n",
    "\n",
    "Here we use several transforms to augment the dataset:\n",
    "1. `LoadImaged` loads the spleen CT images and labels from NIfTI format files.\n",
    "1. `EnsureChannelFirstd` ensures the original data to construct \"channel first\" shape.\n",
    "1. `Spacingd` adjusts the spacing by `pixdim=(1.5, 1.5, 2.)` based on the affine matrix.\n",
    "1. `Orientationd` unifies the data orientation based on the affine matrix.\n",
    "1. `ScaleIntensityRanged` extracts intensity range [-57, 164] and scales to [0, 1].\n",
    "1. `CropForegroundd` removes all zero borders to focus on the valid body area of the images and labels.\n",
    "1. `RandCropByPosNegLabeld` randomly crop patch samples from big image based on pos / neg ratio.  \n",
    "The image centers of negative samples must be in valid body area.\n",
    "1. `RandAffined` efficiently performs `rotate`, `scale`, `shear`, `translate`, etc. together based on PyTorch affine transform.\n",
    "1. `EnsureTyped` converts the numpy array to PyTorch Tensor for further steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6fdde350-2daf-4607-bd96-5e76fefd2335",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yawei/.local/lib/python3.10/site-packages/monai/utils/deprecate_utils.py:321: FutureWarning: monai.transforms.croppad.dictionary CropForegroundd.__init__:allow_smaller: Current default value of argument `allow_smaller=True` has been deprecated since version 1.2. It will be changed to `allow_smaller=False` in version 1.5.\n",
      "  warn_deprecated(argname, msg, warning_category)\n"
     ]
    }
   ],
   "source": [
    "train_transforms = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "        Spacingd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            pixdim=(1.5, 1.5, 2.0),\n",
    "            mode=(\"bilinear\", \"nearest\"),\n",
    "        ),\n",
    "        ScaleIntensityRanged(\n",
    "            keys=[\"image\"],\n",
    "            a_min=-175,\n",
    "            a_max=250,\n",
    "            b_min=0.0,\n",
    "            b_max=1.0,\n",
    "            clip=True,\n",
    "        ),\n",
    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\"),\n",
    "        RandCropByPosNegLabeld(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            label_key=\"label\",\n",
    "            spatial_size=(32,32,32),\n",
    "            pos=1,\n",
    "            neg=1,\n",
    "            num_samples=4,\n",
    "            image_key=\"image\",\n",
    "            image_threshold=0,\n",
    "            allow_smaller=True\n",
    "        ),\n",
    "        RandFlipd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            spatial_axis=[0],\n",
    "            prob=0.10,\n",
    "        ),\n",
    "        RandFlipd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            spatial_axis=[1],\n",
    "            prob=0.10,\n",
    "        ),\n",
    "        RandFlipd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            spatial_axis=[2],\n",
    "            prob=0.10,\n",
    "        ),\n",
    "        RandRotate90d(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            prob=0.10,\n",
    "            max_k=3,\n",
    "        ),\n",
    "        RandShiftIntensityd(\n",
    "            keys=[\"image\"],\n",
    "            offsets=0.10,\n",
    "            prob=0.50,\n",
    "        )\n",
    "    ]\n",
    ")\n",
    "\n",
    "val_transforms = Compose(\n",
    "    [\n",
    "        LoadImaged(keys=[\"image\", \"label\"]),\n",
    "        EnsureChannelFirstd(keys=[\"image\", \"label\"]),\n",
    "        Orientationd(keys=[\"image\", \"label\"], axcodes=\"RAS\"),\n",
    "        Spacingd(\n",
    "            keys=[\"image\", \"label\"],\n",
    "            pixdim=(1.5, 1.5, 2.0),\n",
    "            mode=(\"bilinear\", \"nearest\"),\n",
    "        ),\n",
    "        ScaleIntensityRanged(keys=[\"image\"], a_min=-175, a_max=250, b_min=0.0, b_max=1.0, clip=True),\n",
    "        CropForegroundd(keys=[\"image\", \"label\"], source_key=\"image\")\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74f4e6cb-0e95-4838-8da3-b04f3f354e3d",
   "metadata": {},
   "source": [
    "# Check transforms in DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9287e1b9-a206-4c77-b833-122894929783",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "image shape: torch.Size([171, 171, 128]), label shape: torch.Size([171, 171, 128])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "check_ds = Dataset(data=val_files, transform=val_transforms)\n",
    "check_loader = DataLoader(check_ds, batch_size=1,collate_fn=pad_list_data_collate)\n",
    "check_data = first(check_loader)\n",
    "image, label = (check_data[\"image\"][0][0], check_data[\"label\"][0][0])\n",
    "print(f\"image shape: {image.shape}, label shape: {label.shape}\")\n",
    "# plot the slice [:, :, 80]\n",
    "\n",
    "plt.figure(\"check\", (12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title(\"image\")\n",
    "plt.imshow(image[:, :, 30], cmap=\"gray\")\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title(\"label\")\n",
    "plt.imshow(label[:, :, 30])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "3ec4e394-4059-4483-837d-350cadd30107",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 1, 171, 171, 128])\n"
     ]
    }
   ],
   "source": [
    "check_ds = Dataset(data=val_files, transform=val_transforms)\n",
    "check_loader = DataLoader(check_ds, batch_size=1,collate_fn=pad_list_data_collate)\n",
    "for v in check_loader:\n",
    "    print(v[\"image\"].shape)\n",
    "    break"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "610425c0-e555-4418-8aba-cc6d96193ebc",
   "metadata": {},
   "source": [
    "## Define CacheDataset and DataLoader for training and validation\n",
    "\n",
    "Here we use CacheDataset to accelerate training and validation process, it's 10x faster than the regular Dataset.  \n",
    "To achieve best performance, set `cache_rate=1.0` to cache all the data, if memory is not enough, set lower value.  \n",
    "Users can also set `cache_num` instead of `cache_rate`, will use the minimum value of the 2 settings.  \n",
    "And set `num_workers` to enable multi-threads during caching.  \n",
    "If want to to try the regular Dataset, just change to use the commented code below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "78482497-444a-4725-b323-f82fa8ef8260",
   "metadata": {},
   "outputs": [],
   "source": [
    "# train_ds = CacheDataset(data=train_files, transform=train_transforms, cache_rate=1.0, num_workers=4)\n",
    "train_ds = Dataset(data=train_files, transform=train_transforms)\n",
    "\n",
    "# use batch_size=2 to load images and use RandCropByPosNegLabeld\n",
    "# to generate 2 x 4 images for network training\n",
    "train_loader = DataLoader(train_ds, batch_size=100, shuffle=True, num_workers=1,collate_fn=pad_list_data_collate)\n",
    "\n",
    "# val_ds = CacheDataset(data=val_files, transform=val_transforms, cache_rate=1.0, num_workers=4)\n",
    "val_ds = Dataset(data=val_files, transform=val_transforms)\n",
    "val_loader = DataLoader(val_ds, batch_size=1, num_workers=1,collate_fn=pad_list_data_collate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf7ad082-d8e4-446f-9793-23b316b4a18d",
   "metadata": {},
   "source": [
    "## Create Model, Loss, Optimizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "0614b18f-7e61-4448-a9c8-e2635ed476ec",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataset_name = 'Task_vertebrae'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c95af0a2-f4bd-465c-8755-9051903bd8b7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yawei/.local/lib/python3.10/site-packages/monai/utils/deprecate_utils.py:221: FutureWarning: monai.networks.nets.unetr UNETR.__init__:pos_embed: Argument `pos_embed` has been deprecated since version 1.2. It will be removed in version 1.4. please use `proj_type` instead.\n",
      "  warn_deprecated(argname, msg, warning_category)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Task_vertebrae_UNETR\n"
     ]
    }
   ],
   "source": [
    "# standard PyTorch program style: create UNet, DiceLoss and Adam optimizer\n",
    "#device = torch.device(\"cuda:0\")\n",
    "device = torch.device(\"cpu\")\n",
    "net_metadata = {\n",
    "    \"spatial_dims\": 3,\n",
    "    \"in_channels\": 1,\n",
    "    \"out_channels\": 1,\n",
    "    \"img_size\": (32,32,32),\n",
    "    \"mlp_dim\": 3072,\n",
    "    #  \"strides\": (2, 2, 2, 2),\n",
    "    # \"num_res_units\": 2,\n",
    "    \"feature_size\":16,\n",
    "    \"hidden_size\":768,\n",
    "    \"num_heads\":12,\n",
    "    \"pos_embed\":\"perceptron\",\n",
    "    \"norm_name\":\"instance\",\n",
    "    \"res_block\":True,\n",
    "    # \"norm\": Norm.BATCH,\n",
    "}\n",
    "\n",
    "model = UNETR(**net_metadata).to(device)\n",
    "loss_function = DiceLoss(to_onehot_y=True, softmax=True)\n",
    "loss_type = \"DiceLoss\"\n",
    "optimizer = torch.optim.Adam(model.parameters(), 1e-4)\n",
    "dice_metric = DiceMetric(include_background=False, reduction=\"mean\")\n",
    "\n",
    "Optimizer_metadata = {}\n",
    "for ind, param_group in enumerate(optimizer.param_groups):\n",
    "    optim_meta_keys = list(param_group.keys())\n",
    "    Optimizer_metadata[f\"param_group_{ind}\"] = {\n",
    "        key: value for (key, value) in param_group.items() if \"params\" not in key\n",
    "    }\n",
    "aim_run = aim.Run()\n",
    "aim_run.name = f'{dataset_name}_{model.__class__.__name__}'\n",
    "# log model metadata\n",
    "aim_run[f\"{model.__class__.__name__}_meatdata\"] = net_metadata\n",
    "# log optimizer metadata\n",
    "aim_run[\"Optimizer_metadata\"] = Optimizer_metadata\n",
    "print(aim_run.name)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6288d627-05de-432a-a300-ba5085605eb3",
   "metadata": {},
   "source": [
    "# Execute a typical PyTorch training process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cb0c944c-3c53-48be-ad83-ec368e797774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\nl = 0\\nfor batch_data in train_loader:\\n    if l >10:\\n        break\\n    inputs, labels = (\\n        batch_data[\"image\"].to(device),\\n        batch_data[\"label\"].to(device),\\n    )\\n    print(inputs.shape, labels.shape)\\n    l += 1\\n'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'''\n",
    "l = 0\n",
    "for batch_data in train_loader:\n",
    "    if l >10:\n",
    "        break\n",
    "    inputs, labels = (\n",
    "        batch_data[\"image\"].to(device),\n",
    "        batch_data[\"label\"].to(device),\n",
    "    )\n",
    "    print(inputs.shape, labels.shape)\n",
    "    l += 1\n",
    "'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "370df6a0-a598-49cb-8cc3-663b033b7880",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/mnt/datawow/kw/verse'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "root_dir = os.getcwd()\n",
    "root_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "196a6678-f575-4f4c-b39a-28356c0c8acb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----------\n",
      "epoch 1/2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yawei/.local/lib/python3.10/site-packages/monai/losses/dice.py:147: UserWarning: single channel prediction, `softmax=True` ignored.\n",
      "  warnings.warn(\"single channel prediction, `softmax=True` ignored.\")\n",
      "/home/yawei/.local/lib/python3.10/site-packages/monai/losses/dice.py:156: UserWarning: single channel prediction, `to_onehot_y=True` ignored.\n",
      "  warnings.warn(\"single channel prediction, `to_onehot_y=True` ignored.\")\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/0, train_loss: 0.5090\n",
      "epoch 1 average loss: 0.5090\n",
      "saved new best metric model at the 1th epoch\n",
      "current epoch: 1 current mean dice: 0.0000 \n",
      "best mean dice: 0.0000  at epoch: 1\n",
      "----------\n",
      "epoch 2/2\n",
      "1/0, train_loss: 0.3982\n",
      "epoch 2 average loss: 0.3982\n",
      "current epoch: 2 current mean dice: 0.0000 \n",
      "best mean dice: 0.0000  at epoch: 1\n"
     ]
    }
   ],
   "source": [
    "max_epochs = 2\n",
    "val_interval = 1\n",
    "best_metric = -1\n",
    "best_metric_epoch = -1\n",
    "epoch_loss_values = []\n",
    "metric_values = []\n",
    "post_pred = Compose([AsDiscrete(argmax=True, to_onehot=2)])\n",
    "post_label = Compose([AsDiscrete(to_onehot=2)])\n",
    "\n",
    "slice_to_track = 30\n",
    "\n",
    "for epoch in range(max_epochs):\n",
    "    print(\"-\" * 10)\n",
    "    print(f\"epoch {epoch + 1}/{max_epochs}\")\n",
    "    model.train()\n",
    "    epoch_loss = 0\n",
    "    step = 0\n",
    "    for batch_data in train_loader:\n",
    "        step += 1\n",
    "        inputs, labels = (\n",
    "            batch_data[\"image\"].to(device),\n",
    "            batch_data[\"label\"].to(device),\n",
    "        )\n",
    "        \n",
    "        optimizer.zero_grad()\n",
    "        outputs = model(inputs)\n",
    "        \n",
    "        loss = loss_function(outputs, labels)\n",
    "        loss.backward()\n",
    "        optimizer.step()\n",
    "        epoch_loss += loss.item()\n",
    "        print(f\"{step}/{len(train_ds) // train_loader.batch_size}, \" f\"train_loss: {loss.item():.4f}\")\n",
    "        # track batch loss metric\n",
    "        aim_run.track(loss.item(), name=\"batch_loss\", context={\"type\": loss_type})\n",
    "\n",
    "    epoch_loss /= step\n",
    "    epoch_loss_values.append(epoch_loss)\n",
    "\n",
    "    # track epoch loss metric\n",
    "    aim_run.track(epoch_loss, name=\"epoch_loss\", context={\"type\": loss_type})\n",
    "\n",
    "    print(f\"epoch {epoch + 1} average loss: {epoch_loss:.4f}\")\n",
    "\n",
    "    if (epoch + 1) % val_interval == 0:\n",
    "        if (epoch + 1) % val_interval * 2 == 0:\n",
    "            # track model params and gradients\n",
    "            track_params_dists(model, aim_run)\n",
    "            # THIS SEGMENT TAKES RELATIVELY LONG (Advise Against it)\n",
    "            track_gradients_dists(model, aim_run)\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            for index, val_data in enumerate(val_loader):\n",
    "                val_inputs, val_labels = (\n",
    "                    val_data[\"image\"].to(device),\n",
    "                    val_data[\"label\"].to(device),\n",
    "                )\n",
    "                # roi_size = (160, 160, 160)\n",
    "                roi_size = (32, 32, 32)\n",
    "\n",
    "                sw_batch_size = 4\n",
    "                val_outputs = sliding_window_inference(val_inputs, roi_size, sw_batch_size, model)\n",
    "\n",
    "                # tracking input, label and output images with Aim\n",
    "                output = torch.argmax(val_outputs, dim=1)[0, :, :, slice_to_track].float()\n",
    "\n",
    "                aim_run.track(\n",
    "                    aim.Image(val_inputs[0, 0, :, :, slice_to_track], caption=f\"Input Image: {index}\"),\n",
    "                    name=\"validation\",\n",
    "                    context={\"type\": \"input\"},\n",
    "                )\n",
    "                aim_run.track(\n",
    "                    aim.Image(val_labels[0, 0, :, :, slice_to_track], caption=f\"Label Image: {index}\"),\n",
    "                    name=\"validation\",\n",
    "                    context={\"type\": \"label\"},\n",
    "                )\n",
    "                aim_run.track(\n",
    "                    aim.Image(output, caption=f\"Predicted Label: {index}\"),\n",
    "                    name=\"predictions\",\n",
    "                    context={\"type\": \"labels\"},\n",
    "                )\n",
    "\n",
    "                val_labels_list = decollate_batch(val_labels)\n",
    "                val_labels_convert = [post_pred(val_label_tensor) for val_label_tensor in val_labels_list]\n",
    "                val_outputs_list = decollate_batch(val_outputs)\n",
    "                val_output_convert = [post_pred(val_pred_tensor) for val_pred_tensor in val_outputs_list]\n",
    "                dice_metric(y_pred=val_output_convert, y=val_labels_convert)\n",
    "                \n",
    "                #val_outputs = [post_pred(i) for i in decollate_batch(val_outputs)]\n",
    "                #val_labels = [post_label(i) for i in decollate_batch(val_labels)]\n",
    "                # compute metric for current iteration\n",
    "                #dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "\n",
    "            # aggregate the final mean dice result\n",
    "            metric = dice_metric.aggregate().item()\n",
    "            # track val metric\n",
    "            aim_run.track(metric, name=\"val_metric\", context={\"type\": loss_type})\n",
    "\n",
    "            # reset the status for next validation round\n",
    "            dice_metric.reset()\n",
    "\n",
    "            metric_values.append(metric)\n",
    "            if metric > best_metric:\n",
    "                best_metric = metric\n",
    "                best_metric_epoch = epoch + 1\n",
    "                torch.save(model.state_dict(), os.path.join(root_dir, f\"{aim_run.name}_best_metric_model.pth\"))\n",
    "\n",
    "                best_model_log_message = f\"saved new best metric model at the {epoch+1}th epoch\"\n",
    "                aim_run.track(aim.Text(best_model_log_message), name=\"best_model_log_message\", epoch=epoch + 1)\n",
    "                print(best_model_log_message)\n",
    "\n",
    "            message1 = f\"current epoch: {epoch + 1} current mean dice: {metric:.4f}\"\n",
    "            message2 = f\"\\nbest mean dice: {best_metric:.4f} \"\n",
    "            message3 = f\"at epoch: {best_metric_epoch}\"\n",
    "\n",
    "            aim_run.track(aim.Text(message1 + \"\\n\" + message2 + message3), name=\"epoch_summary\", epoch=epoch + 1)\n",
    "            print(message1, message2, message3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dc21b104-ac8c-4d14-bc1b-20c4190e60a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# finalize Aim Run\n",
    "aim_run.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "61bf8c99-9a99-4195-9c9d-e9c769df2911",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metric on original image spacing:  0.0\n"
     ]
    }
   ],
   "source": [
    "model.load_state_dict(torch.load(os.path.join(root_dir, f\"{aim_run.name}_best_metric_model.pth\")))\n",
    "model.eval()\n",
    "\n",
    "post_transforms = Compose(\n",
    "    [\n",
    "        Invertd(\n",
    "            keys=\"pred\",\n",
    "            transform=val_org_transforms,\n",
    "            orig_keys=\"image\",\n",
    "            meta_keys=\"pred_meta_dict\",\n",
    "            orig_meta_keys=\"image_meta_dict\",\n",
    "            meta_key_postfix=\"meta_dict\",\n",
    "            nearest_interp=False,\n",
    "            to_tensor=True,\n",
    "        ),\n",
    "        AsDiscreted(keys=\"label\", to_onehot=2),\n",
    "    ]\n",
    ")\n",
    "\n",
    "\n",
    "with torch.no_grad():\n",
    "    for val_data in val_loader:\n",
    "        val_data[\"image\"] = val_data[\"image\"].to(device)\n",
    "        val_data[\"label\"] = val_data[\"label\"].to(device)\n",
    "        roi_size = (32, 32, 32)\n",
    "        sw_batch_size = 4\n",
    "        val_data[\"pred\"] = sliding_window_inference(val_data[\"image\"], roi_size, sw_batch_size, model)\n",
    "        val_data = [i for i in decollate_batch(val_data)]\n",
    "        val_outputs, val_labels = from_engine([\"pred\", \"label\"])(val_data)\n",
    "        # compute metric for current iteration\n",
    "        dice_metric(y_pred=val_outputs, y=val_labels)\n",
    "\n",
    "    # aggregate the final mean dice result\n",
    "    metric_org = dice_metric.aggregate().item()\n",
    "    # reset the status for next validation round\n",
    "    dice_metric.reset()\n",
    "\n",
    "print(\"Metric on original image spacing: \", metric_org)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "409e681e-f1d5-433e-9610-575498835383",
   "metadata": {},
   "outputs": [],
   "source": [
    "## Evaluation on original image spacings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e5f0103-9983-4a11-aba4-b746b1019fbf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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